A wide variety of locomotion gaits have been demonstrated on quadruped robots, including walking [1]}, trotting [2]}, pacing [3]}, bounding [4]} and galloping [5]}. In these works, model-based controllers [2]}, [7]} optimizes for motor commands at a high frequency using model predictive control techniques. Despite their robustness, these controllers usually assume a pre-defined contact sequence to keep the optimization problem tractable, and do not actively search for an efficient gait. Although contact-implicit optimization [8]}, [9]}, [10]} can optimize contact forces and sequences together, the extra computation cost makes it difficult for real-time use. [11]} achieves online gait transition by computing the Feasible Impulse Set for each leg, up to 0.6m/s, or 1 body length/s.
[12]} also used foot-force heuristics for gait transition on a 2kg quadruped robot. Compared to these approaches, our learning-based approach is more automated, achieves more agile motions (up to 2.5m/s, or 5 body length/s) on a big robot (15kg).
